Predicted and actual 2-year structural and pain progression in the IMI-APPROACH knee osteoarthritis cohort

Eefje M van Helvoort, Mylène P Jansen, Anne C A Marijnissen, Margreet Kloppenburg, Francisco J Blanco, Ida K Haugen, Francis Berenbaum, Anne-Christine C Bay-Jensen, Christoph Ladel, Agnes Lalande, Jonathan Larkin, John Loughlin, Ali Mobasheri, Harrie H Weinans, Pawel Widera, Jaume Bacardit, Paco M J Welsing, Floris P J G Lafeber, Eefje M van Helvoort, Mylène P Jansen, Anne C A Marijnissen, Margreet Kloppenburg, Francisco J Blanco, Ida K Haugen, Francis Berenbaum, Anne-Christine C Bay-Jensen, Christoph Ladel, Agnes Lalande, Jonathan Larkin, John Loughlin, Ali Mobasheri, Harrie H Weinans, Pawel Widera, Jaume Bacardit, Paco M J Welsing, Floris P J G Lafeber

Abstract

Objectives: The IMI-APPROACH knee osteoarthritis study used machine learning (ML) to predict structural and/or pain progression, expressed by a structural (S) and pain (P) predicted-progression score, to select patients from existing cohorts. This study evaluates the actual 2-year progression within the IMI-APPROACH, in relation to the predicted-progression scores.

Methods: Actual structural progression was measured using minimum joint space width (minJSW). Actual pain (progression) was evaluated using the Knee injury and Osteoarthritis Outcomes Score (KOOS) pain questionnaire. Progression was presented as actual change (Δ) after 2 years, and as progression over 2 years based on a per patient fitted regression line using 0, 0.5, 1 and 2-year values. Differences in predicted-progression scores between actual progressors and non-progressors were evaluated. Receiver operating characteristic (ROC) curves were constructed and corresponding area under the curve (AUC) reported. Using Youden's index, optimal cut-offs were chosen to enable evaluation of both predicted-progression scores to identify actual progressors.

Results: Actual structural progressors were initially assigned higher S predicted-progression scores compared with structural non-progressors. Likewise, actual pain progressors were assigned higher P predicted-progression scores compared with pain non-progressors. The AUC-ROC for the S predicted-progression score to identify actual structural progressors was poor (0.612 and 0.599 for Δ and regression minJSW, respectively). The AUC-ROC for the P predicted-progression score to identify actual pain progressors were good (0.817 and 0.830 for Δ and regression KOOS pain, respectively).

Conclusion: The S and P predicted-progression scores as provided by the ML models developed and used for the selection of IMI-APPROACH patients were to some degree able to distinguish between actual progressors and non-progressors.

Trial registration: ClinicalTrials.gov, https://ichgcp.net/clinical-trials-registry/NCT03883568" title="See in ClinicalTrials.gov">NCT03883568.

Keywords: Knee osteoarthritis; biomarkers; clinical trials and methods; study design.

© The Author(s) 2022. Published by Oxford University Press on behalf of the British Society for Rheumatology.

Figures

Fig. 1
Fig. 1
S predicted-progression score of actual radiographic progressors and non-progressors (A) S predicted-progression scores for actual radiographic progressors (absolute decrease in 2 years ≥0.6 mm, n = 41) and non-progressors (n = 183). (B) S predicted-progression scores for actual radiographic progressors (regression of each patient ≥0.6 mm/2 years, n = 63) and non-progressors (n = 203). minJSW: minimum joint space width.
Fig. 2
Fig. 2
P predicted-progression score of actual pain progressors and non-progressors (A) P predicted-progression scores for actual pain progressors (n = 65; black) and non-progressors (n = 181; grey), as well as for patients with pain increase (n = 25; dotted) and patients with stable significant pain (n = 40; dashed) using the absolute decrease during the 2-year follow-up period. (B) P predicted-progression scores for actual pain progressors (n = 67; black) and non-progressors (n = 179; grey), as well as for patients with pain increase (n = 28; dotted line) and patients with stable significant pain (n = 39; dashed line) using the regression over 2 years of each individual patient. KOOS: Knee injury and Osteoarthritis Outcomes Score.
Fig. 3
Fig. 3
ROC-curves S predicted-progression score ROC curves for ΔminJSW (A) and regression minJSW (B). AUC: area under the curve; minJSW: minimum joint space width; ROC: receiver operating characteristic; Sen: sensitivity; Spec: specificity.
Fig. 4
Fig. 4
ROC curves P predicted-progression score ROC curves for ΔKOOS pain and regression KOOS pain for total progressors (A, B), patients with pain increase (C, D), and patients with stable significant pain (E, F). AUC: area under the curve; KOOS: Knee injury and Osteoarthritis Outcomes Score; ROC: receiver operating characteristic; Sen: sensitivity; Spec: specificity.

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Source: PubMed

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